Andy1621
commented
12 months ago Here are the code for accuracy： https://github.com/OpenGVLab/UniFormerV2/blob/722a43440fc5b9662cc2a8f23b86caa205e45ebc/slowfast/utils/metrics.py#L9-L64
The below code is modified by GPT, please try it
def mean_recall(preds, labels, num_classes):
"""
Calculate the mean recall given predictions and labels.
Args:
preds (Tensor): Predictions from the model. Dimension is N x ClassNum.
labels (Tensor): True labels. Dimension is N.
num_classes (int): Number of classes.
Returns:
mean_recall (float): The mean recall over all classes.
"""
assert preds.size(0) == labels.size(0), "Batch dim of predictions and labels must match"
# Convert predictions to class indices
_, predicted_classes = preds.max(dim=1)
# Initialize TP and FN counters
TP = [0] * num_classes
FN = [0] * num_classes
# Count TP and FN for each class
for i in range(num_classes):
TP[i] = ((predicted_classes == i) & (labels == i)).sum().item()
FN[i] = ((predicted_classes != i) & (labels == i)).sum().item()
# Calculate recall for each class
recalls = [TP[i] / (TP[i] + FN[i]) if (TP[i] + FN[i]) > 0 else 0 for i in range(num_classes)]
# Calculate mean recall
mean_recall = sum(recalls) / num_classes
return mean_recall
ZachWong123
I've observed a common practice in video understanding models and research papers where the recall rate is essentially not provided. Recently, my teacher assigned me a project that requires both accuracy (acc) and recall (AR). When I explained to him that papers usually don't include recall, he suggested running the code and modifying it to output the recall rate. However, I'm uncertain about how to proceed with this. I would greatly appreciate your prompt response. Thank you!